Uploaded January 2015 | Updated September 2026, 2 hours ago
Usually, Venn diagrams are not very useful for illustrating independence, as the sizes of the circles and their intersections have no meaning. It can help to illustrate independence if we force the area of each region to be equal to its probability of occurring. Independence is even easier to see if we represent the events with rectangles instead of circles. I illustrate these concepts in this video.
All plots were created in R. The appropriate diameters of the circles and distance between the centres of the circles were calculated in R using the package venneuler.
Not all plots in this video are officially called Venn diagrams.
Usually, Venn diagrams are not very useful for illustrating independence, as the sizes of the circles and their intersections have no meaning. It can help to illustrate independence if we force the area of each region to be equal to its probability of occurring. Independence is even easier to see if we represent the events with rectangles instead of circles. I illustrate these concepts in this video.
All plots were created in R. The appropriate diameters of the circles and distance between the centres of the circles were calculated in R using the package venneuler.
Not all plots in this video are officially called Venn diagrams.

![The Sampling Distribution of the Sample Mean
I discuss the sampling distribution of the sample mean, and work through an example of a probability calculation. (I only briefly mention the central limit theorem here, but discuss it in more detail in another video).
The mean and standard deviation of the amount of protein in a quarter pound patty of lean beef was found in the USDA nutrient database at:
http://ndb.nal.usda.gov/ndb/foods/show/7413?fg=&man=&lfacet=&format=&count=&max=25&offset=&sort=&qlookup=beef+hamburger
For those using R, here is the R code to find the values in the examples:
The probability a randomly selected patty has at least 23.0 grams of protein (mu = 21.4, sigma = 1.9):
1-pnorm(23.0,21.4,1.9)
[1] 0.1998645
or, if we standardize:
1-pnorm((23.0-21.4)/1.9)
[1] 0.1998645
The probability that the mean of 4 randomly selected patties is at least 23.0 grams of protein (sampling from a normal distribution with mu = 21.4, sigma = 1.9):
1-pnorm(23.0,21.4,1.9/sqrt(4))
[1] 0.04607049
or, if we standardize:
1-pnorm((23.0-21.4)/(1.9/sqrt(4)))
[1] 0.04607049 The Sampling Distribution of the Sample Mean](https://i.ytimg.com/vi/q50GpTdFYyI/mqdefault.jpg)

![An Introduction to the Binomial Distribution
An introduction to the binomial distribution. I discuss the conditions required for a random variable to have a binomial distribution, discuss the binomial probability mass function and the mean and variance, and look at two examples involving probability calculations.
The estimated probability of a 90 year old Canadian male surviving for one year was taken from Statistics Canada life tables, which can be found at http://www.statcan.gc.ca/pub/84-537-x/4064441-eng.htm. The probability given in the table is the estimated probability that a randomly selected Canadian male, given survival to his 90th birthday, survives until his 91st. I simplified this explanation a little in the example in the video.
For those using R, here is the R code to find the probabilities for the examples in this video:
Die roll example.
Finding the probability of getting exactly two fives in three rolls:
dbinom(2,3,1/6)
[1] 0.06944444
Twenty randomly sampled 90-year old Canadian males example.
Finding the probability that exactly 18 survive for at least a year:
dbinom(18,20,.82)
[1] 0.1729609
Finding the probability that at least 18 survive for at least a year:
dbinom(18,20,.82)+dbinom(19,20,.82)+dbinom(20,20,.82)
[1] 0.2747932
or
1-pbinom(17,20,.82)
[1] 0.2747932 An Introduction to the Binomial Distribution](https://i.ytimg.com/vi/qIzC1-9PwQo/mqdefault.jpg)






